
from dbmsbenchmarker import *
import pandas as pd
pd.set_option("max_rows", None)

# Some plotly figures
import plotly.graph_objects as go
import plotly.figure_factory as ff

# Some nice output
from IPython.display import display, Markdown

import logging
logging.basicConfig(level=logging.INFO)


# path of folder containing experiment results
resultfolder = "results/"

# create evaluation object for result folder
evaluate = inspector.inspector(resultfolder)

# list of all experiments in folder
# evaluate.list_experiments
# dataframe of experiments
evaluate.get_experiments_preview()


# last Experiment
code = evaluate.list_experiments[len(evaluate.list_experiments)-1]

# Specific Experiment
code = '1625255968'

# load it
evaluate.load_experiment(code)

Benchmarking in folder results//1625255968
Connections:
MariaDB-2-1
PostgreSQL-1-1
MonetDB-1-1
MySQL-1-1
MariaDB-1-1
MySQL-2-1
PostgreSQL-2-1
MonetDB-2-1
Queries:
0: Q1 = Pricing Summary Report (TPC-H Q1)
1: Q2 = Minimum Cost Supplier Query (TPC-H Q2)
2: Q3 = Shipping Priority (TPC-H Q3)
3: Q4 = Order Priority Checking Query (TPC-H Q4)
4: Q5 = Local Supplier Volume (TPC-H Q5)
5: Q6 = Forecasting Revenue Change (TPC-H Q6)
6: Q7 = Forecasting Revenue Change (TPC-H Q7)
7: Q8 = National Market Share (TPC-H Q8)
8: Q9 = Product Type Profit Measure (TPC-H Q9)
9: Q10 = Forecasting Revenue Change (TPC-H Q10)
10: Q11 = Important Stock Identification (TPC-H Q11)
11: Q12 = Shipping Modes and Order Priority (TPC-H Q12)
12: Q13 = Customer Distribution (TPC-H Q13)
13: Q14 = Forecasting Revenue Change (TPC-H Q14)
14: Q15 = Top Supplier Query (TPC-H Q15)
15: Q16 = Parts/Supplier Relationship (TPC-H Q16)
16: Q17 = Small-Quantity-Order Revenue (TPC-H Q17)
17: Q18 = Large Volume Customer (TPC-H Q18)
18: Q19 = Discounted Revenue (TPC-H Q19)
19: Q20 = Potential Part Promotion (TPC-H Q20)
20: Q21 = Suppliers Who Kept Orders Waiting Query (TPC-H Q21)
21: Q22 = Global Sales Opportunity Query (TPC-H Q22)
Load Evaluation


# get experiment workflow
df = evaluate.get_experiment_workflow()
#print(df)

# get workload properties
workload_properties = evaluate.get_experiment_workload_properties()
print(workload_properties['name'])
print(workload_properties['info'])

# list queries
list_queries = evaluate.get_experiment_list_queries()

# list connections
list_nodes = evaluate.get_experiment_list_nodes()
list_dbms = evaluate.get_experiment_list_dbms()
list_connections = evaluate.get_experiment_list_connections()
list_connections_node = evaluate.get_experiment_list_connections_by_node()
list_connections_dbms = evaluate.get_experiment_list_connections_by_dbms()
list_connections_clients = evaluate.get_experiment_list_connections_by_connectionmanagement('numProcesses')
list_connections_gpu = evaluate.get_experiment_list_connections_by_hostsystem('GPU')

# colors by dbms
list_connections_dbms = evaluate.get_experiment_list_connections_by_dbms()
connection_colors = evaluate.get_experiment_list_connection_colors(list_connections_dbms)

# fix some examples:
# first connection, first query, first run
connection = list_connections[0]
numQuery = 1
numRun = 0

TPC-H Queries SF=10
This experiment compares run time and resource consumption of TPC-H queries in different DBMS.


connection = 'MonetDB-1-1'

display(Markdown("### Properties of {}".format(connection)))

evaluator.pretty(evaluate.get_experiment_connection_properties(connection))

name:MonetDB-1-1
script:SF10-index
docker:MonetDB
docker_alias:Columnwise
version:11.37.11
info:[]
connectionmanagement
  numProcesses:1
  runsPerConnection:Unlimited
  timeout:3600
hostsystem
  RAM:1082007052288
  CPU:AMD EPYC 7742 64-Core Processor
  GPU:
  GPUIDs:[]
  Cores:256
  host:5.4.0-74-generic
  node:cl-worker28
  disk:623220008
  datadisk:10687696
  cuda:
  requests_cpu:4
  requests_memory:16Gi
  limits_cpu:0
  limits_memory:0
worker:[]
times
  load_ms:251632.57140340284
  benchmark_ms:316155.41979367845
prices
  perHour_usd:0.0
  benchmark_usd:0.0
hardwaremetrics
  total_cpu_memory:0
  total_cpu_memory_cached:0
  total_cpu_util:0
  total_gpu_util:0
  total_gpu_power:0
  total_gpu_memory:0
  total_cpu_throttled:0
  total_cpu_util_others:0
  total_cpu_util_s:0
  total_cpu_util_user_s:0
  total_cpu_util_sys_s:0
  total_cpu_throttled_s:0
  total_cpu_util_others_s:0
  total_network_rx:0
  total_network_tx:0
  total_fs_read:0
  total_fs_write:0
  total_gpu_energy:0.0
metrics
  totaltime_ms:3470.0931945159236
  throughput_run_total_ps:0.8645300952554098
  throughput_run_total_ph:3112.308342919476
  throughput_session_total_ps:0.28817669841846993
  throughput_session_total_ph:1037.436114306492
  throughput_run_mean_ps:0.924158961737484
  throughput_run_mean_ph:3326.972262254943
  latency_run_mean_ms:1082.0649275747212
  throughput_session_mean_ps:0.30805298724582797
  throughput_session_mean_ph:1108.990754084981
  latency_session_mean_ms:3246.194782724164
  queuesize_run:0.9354776949087117
  queuesize_run_percent:93.54776949087119
  queuesize_session:0.9354776949087117
  queuesize_session_percent:93.54776949087118


connection = list_connections[0]
numQuery = 1
numRun = 0


list_errors = evaluate.get_error(numQuery)

display(Markdown("### Errors of Query {}".format(numQuery)))
pd.DataFrame.from_dict(list_errors, orient='index').sort_index()


list_errors = evaluate.get_warning(numQuery)

display(Markdown("### Warnings of Query {}".format(numQuery)))
pd.DataFrame.from_dict(list_errors, orient='index').sort_index()


query_properties = evaluate.get_experiment_query_properties()

display(Markdown("#### Show Query Template {} - {}".format(numQuery, query_properties[str(numQuery)]['config']['title'])))
print(query_properties[str(numQuery)]['config']['query'])

select
l_returnflag,
l_linestatus,
cast(sum(l_quantity) as bigint) as sum_qty,
sum(l_extendedprice) as sum_base_price,
sum(l_extendedprice*(1-l_discount)) as sum_disc_price,
sum(l_extendedprice*(1-l_discount)*(1+l_tax)) as sum_charge,
avg(l_quantity) as avg_qty,
avg(l_extendedprice) as avg_price,
avg(l_discount) as avg_disc,
count(*) as count_order
from
lineitem
where
l_shipdate <= date '1998-12-01' - interval '{DELTA}'  day
group by
l_returnflag,
l_linestatus
order by
l_returnflag,
l_linestatus


display(Markdown("#### Show Parameters of Query {} - {}".format(numQuery, query_properties[str(numQuery)]['config']['title'])))

df = evaluate.get_parameter_df(numQuery)
df


display(Markdown("#### Show Query {} as run by {} - Run number {}".format(numQuery, connection, numRun)))

query_string = evaluate.get_querystring(numQuery, connection, numRun)
print(query_string)

select
l_returnflag,
l_linestatus,
cast(sum(l_quantity) as unsigned int) as sum_qty,
sum(l_extendedprice) as sum_base_price,
sum(l_extendedprice*(1-l_discount)) as sum_disc_price,
sum(l_extendedprice*(1-l_discount)*(1+l_tax)) as sum_charge,
avg(l_quantity) as avg_qty,
avg(l_extendedprice) as avg_price,
avg(l_discount) as avg_disc,
count(*) as count_order
from
lineitem
where
l_shipdate <= date('1998-12-01') - interval '71'  day
group by
l_returnflag,
l_linestatus
order by
l_returnflag,
l_linestatus
limit 10000000


display(Markdown("#### Show Query {} as run by {} - Run number {}".format(numQuery, "MonetDB-1-1", numRun)))

query_string = evaluate.get_querystring(numQuery, connection, numRun)
print(query_string)

select
l_returnflag,
l_linestatus,
cast(sum(l_quantity) as unsigned int) as sum_qty,
sum(l_extendedprice) as sum_base_price,
sum(l_extendedprice*(1-l_discount)) as sum_disc_price,
sum(l_extendedprice*(1-l_discount)*(1+l_tax)) as sum_charge,
avg(l_quantity) as avg_qty,
avg(l_extendedprice) as avg_price,
avg(l_discount) as avg_disc,
count(*) as count_order
from
lineitem
where
l_shipdate <= date('1998-12-01') - interval '71'  day
group by
l_returnflag,
l_linestatus
order by
l_returnflag,
l_linestatus
limit 10000000


display(Markdown("#### Show Result Set of Query {} as run by {} - Run number {}".format(numQuery, connection, numRun)))

df = evaluate.get_datastorage_df(numQuery, numRun)
df


display(Markdown("#### Show Result Set of Query {} as run by {} - Run number {}".format(numQuery, "MonetDB-1-1", numRun)))

df = evaluate.get_resultset_df(numQuery, "MonetDB-1-1", numRun)
df


# list all available metrics
display(Markdown("### Hardware Metrics"))

pd.DataFrame(monitor.metrics.metrics).T


# get hardware metrics for loading test
df = evaluate.get_loading_metrics('total_cpu_memory')
df = evaluate.get_loading_metrics('total_cpu_util_s')
df = df.T.max().sort_index() - df.T.min().sort_index() # compute difference of counter

display(Markdown("### CPU of Ingestion (via counter)"))
pd.DataFrame(df)


df = evaluate.get_loading_metrics('total_cpu_util')
df = df.T.sum().sort_index() # computer sum of rates

display(Markdown("### CPU of Ingestion (via rate)"))
pd.DataFrame(df)


# get hardware metrics per stream
df = evaluate.get_streaming_metrics('total_cpu_memory')
df = evaluate.get_streaming_metrics('total_cpu_util_s')
df = df.T.max().sort_index() - df.T.min().sort_index() # compute difference of counter

display(Markdown("### CPU of Stream (via counter)"))
pd.DataFrame(df)


df = evaluate.get_streaming_metrics('total_cpu_util')
df = df.T.sum().sort_index() # computer sum of rates

display(Markdown("### CPU of Stream (via rate)"))
pd.DataFrame(df)


df = evaluate.get_aggregated_experiment_statistics(type='timer', name='run', query_aggregate='Mean', total_aggregate='Mean')
df = (df/1000.0).sort_index()

display(Markdown("### Mean of Means of Timer Run [s]"))
df


df = evaluate.get_aggregated_experiment_statistics(type='timer', name='run', query_aggregate='Median', total_aggregate='Geo')
df = (df/1000.0).sort_index()

display(Markdown("### Geometric Mean of Medians of Timer Run [s]"))
df


%matplotlib inline


df = evaluate.get_aggregated_experiment_statistics(type='timer', name='run', query_aggregate='Median', total_aggregate='Geo')
df = df.sort_index()

fig = go.Figure()
for i in range(len(df.index)):
    t = fig.add_trace(go.Bar(x=[df.index[i]], y=df.iloc[i], name=df.index[i], marker=dict(color=connection_colors[df.index[i]])))

fig.update_layout(title_text='Geometric Mean of Medians of Timer Run [s]')
fig.show()


df = evaluate.get_aggregated_query_statistics(type='timer', name='run', query_aggregate='Mean').sort_index().T

display(Markdown("### Means of Timer Runs [ms]"))
df


df = (evaluate.get_aggregated_query_statistics(type='throughput', name='run', query_aggregate='Max')).sort_index().T

display(Markdown("### Maximum of Run Throughput [1/s]"))
df


df = evaluate.get_aggregated_query_statistics(type='latency', name='execution', query_aggregate='Mean').sort_index().T

display(Markdown("### Latency of Timer Execution [ms]"))
df


df = evaluate.get_aggregated_query_statistics(type='timer', name='connection', query_aggregate='Mean').T

display(Markdown("### Latency of Timer Connection [ms]"))
df

No data


df = evaluate.get_aggregated_query_statistics(type='timer', name='datatransfer', query_aggregate='Mean').T

display(Markdown("### Latency of Timer Data Transfer [ms]"))
df

No data


df = evaluate.get_aggregated_query_statistics(type='timer', name='run', query_aggregate='factor').sort_index().T

display(Markdown("### Latency of Timer Run - normalized to 1 per Query"))
df


df = evaluate.get_total_resultsize().T

display(Markdown("### Size of Result Sets per Query"))
df


df = evaluate.get_total_resultsize_normalized()

display(Markdown("### Size of Result Sets per Query - normalized to 1"))
df


df = evaluate.get_total_errors().T

display(Markdown("### Table of Errors"))
df


df = evaluate.get_total_warnings().T

display(Markdown("### Table of Warnings"))
df


df = evaluate.get_total_times().T/1000.0

display(Markdown("### Total Time [s] per Query"))
df


df = evaluate.get_total_times_normalized().T

display(Markdown("### Total Time per Query - normalized to 100%"))
df


df = evaluate.get_total_times_relative().T

display(Markdown("### Total Time per Query - normalized to 100%"))
df


df = (evaluate.get_aggregated_query_statistics(type='timer', name='run', query_aggregate='Mean').T/1000.0).round(2)
#.sort_index(ascending=False)
#df=df.T
#df=df.round(2)

fig1 = ff.create_annotated_heatmap(
    x=list(df.columns),
    y=list(df.index),
    z=df.values.tolist(),
    showscale=True,
    colorscale='Reds',
    xgap=1,
    ygap=1,
    )

fig1.update_layout(title_text='Timer Run - Mean per Query [s]')
fig1.layout.xaxis.type = 'category'
fig1.layout.yaxis.type = 'category'

fig1.show()


df = (evaluate.get_aggregated_query_statistics(type='timer', name='run', query_aggregate='Std Dev').T/1000.0).round(2)
#.sort_index(ascending=False)
#df=df.T
#df=df.round(2)

fig1 = ff.create_annotated_heatmap(
    x=list(df.columns),
    y=list(df.index),
    z=df.values.tolist(),
    showscale=True,
    colorscale='Reds',
    xgap=1,
    ygap=1,
    )

fig1.update_layout(title_text='Timer Run - Std Dev per Query [s]')
fig1.layout.xaxis.type = 'category'
fig1.layout.yaxis.type = 'category'

fig1.show()


df = evaluate.get_aggregated_query_statistics(type='timer', name='run', query_aggregate='factor').round(2)
df=df.sort_index(ascending=True).T
#df=df.T
#df=df.round(2)

fig1 = ff.create_annotated_heatmap(
    x=list(df.columns),
    y=list(df.index),
    z=df.values.tolist(),
    showscale=True,
    colorscale='Reds',
    xgap=1,
    ygap=1,
    )

fig1.update_layout(title_text='Timer Run - Factor per Query [s]')
fig1.layout.xaxis.type = 'category'
fig1.layout.yaxis.type = 'category'

fig1.show()


numQuery = 4


df1, df2 = evaluate.get_measures_and_statistics(numQuery, type='timer', name='execution')

display(Markdown("### Measures of Execution Times - {} Runs of Query {}".format(len(df1.columns), numQuery)))
df1.sort_index()


display(Markdown("### Statistics of Execution Times - {} Runs of Query {}".format(len(df1.columns), numQuery)))
df2.sort_index()


df1,df2=evaluate.get_measures_and_statistics(numQuery, type='timer', name='run', warmup=0)
df1 = df1.sort_index()

# Plots
fig = go.Figure()
for i in range(len(df1.index)):
    t = fig.add_trace(go.Scatter(x=df1.T.index, y=df1.iloc[i], name=df1.index[i], line=dict(color=connection_colors[df1.index[i]], width=1)))

fig.update_layout(title_text='Timer Run [ms] - Query {} ({} Measures)'.format(numQuery, len(df1.columns)))
fig.show()


# Bar
df1, df2 = evaluate.get_measures_and_statistics(numQuery, type='timer', name='run')
df = tools.dataframehelper.collect(df2, 'Mean', 'timer_run_mean').sort_index()

fig = go.Figure()
for i in range(len(df.index)):
    t = fig.add_trace(go.Bar(x=[df.index[i]], y=df.iloc[i], name=df.index[i], marker=dict(color=connection_colors[df.index[i]])))

fig.update_layout(title_text='Mean of Timer Run [s] - Query {}'.format(numQuery))
fig.show()


# Boxplots
df1, df2 = evaluate.get_measures_and_statistics(numQuery, type='timer', name='run')
df1 = df1.sort_index()

fig = go.Figure()
for i in range(len(df1.index)):
    t = fig.add_trace(go.Box(y=df1.iloc[i], name=df1.index[i], line=dict(color=connection_colors[df1.index[i]], width=1), boxmean='sd'))

fig.update_layout(title_text='Timer Run [ms] - Query {}'.format(numQuery))
fig.show()


# Histogram
numQuery = 1

df1, df2 = evaluate.get_measures_and_statistics(numQuery, type='timer', name='run')
df1=df1.sort_index()

fig = go.Figure(layout = go.Layout(barmode='overlay'))
for i in range(len(df1.index)):
    t = fig.add_trace(go.Histogram(x=df1.iloc[i], name=df1.index[i], opacity=0.75, marker=dict(color=connection_colors[df1.index[i]])))

fig.update_layout(title_text='Timer Run Histogram - Query {}'.format(numQuery))
fig.show()

	L_RETURNFLAG	L_LINESTATUS	SUM_QTY	SUM_BASE_PRICE	SUM_DISC_PRICE	SUM_CHARGE	AVG_QTY	AVG_PRICE	COUNT_ORDER
1	A	F	377518399	566065727797.0	537759104278.0	559276670892.0	26.0	38237.0	14804077
2	N	F	9851614	14767438399.0	14028805792.0	14590490998.0	26.0	38258.0	385998
3	N	O	751203243	1126406784135.0	1070081467451.0	1112897846350.0	25.0	38234.0	29460795
4	R	F	377732830	566431054976.0	538110922665.0	559634780885.0	26.0	38251.0	14808183

	L_RETURNFLAG	L_LINESTATUS	SUM_QTY	SUM_BASE_PRICE	SUM_DISC_PRICE	SUM_CHARGE	AVG_QTY	AVG_PRICE	COUNT_ORDER
1	A	F	377518399	566065727797.0	537759104278.0	559276670892.0	26.0	38237.0	14804077
2	N	F	9851614	14767438399.0	14028805792.0	14590490998.0	26.0	38258.0	385998
3	N	O	751203243	1126406784135.0	1070081467451.0	1112897846350.0	26.0	38234.0	29460795
4	R	F	377732830	566431054976.0	538110922665.0	559634780885.0	26.0	38251.0	14808183

	query	title
total_cpu_memory	(node_memory_MemTotal_bytes-node_memory_MemFre...	CPU Memory [MiB]
total_cpu_memory_cached	(node_memory_Cached_bytes)/1024/1024	CPU Memory Cached [MiB]
total_cpu_util	100 - (avg by (instance) (irate(node_cpu_secon...	CPU Util [%]
total_gpu_util	sum(dcgm_gpu_utilization)	GPU Util [%]
total_gpu_power	sum(dcgm_power_usage)	GPU Power Usage [W]
total_gpu_memory	sum(dcgm_fb_used)	GPU Memory [MiB]
total_cpu_throttled	sum(rate(container_cpu_cfs_throttled_seconds_t...	CPU Throttle [%]
total_cpu_util_others	sum(irate(container_cpu_usage_seconds_total{{j...	CPU Others [%]
total_cpu_util_s	sum(container_cpu_usage_seconds_total{{job="mo...	CPU Util [s]
total_cpu_util_user_s	sum(container_cpu_user_seconds_total{{job="mon...	CPU Util User [s]
total_cpu_util_sys_s	sum(container_cpu_system_seconds_total{{job="m...	CPU Util Sys [s]
total_cpu_throttled_s	sum(container_cpu_cfs_throttled_seconds_total{...	CPU Throttle [s]
total_cpu_util_others_s	sum(container_cpu_usage_seconds_total{{job="mo...	CPU Util Others [s]
total_network_rx	sum(container_network_receive_bytes_total{{con...	Net Rx [b]
total_network_tx	sum(container_network_transmit_bytes_total{{co...	Net Tx [b]
total_fs_read	sum(container_fs_reads_bytes_total{{job="monit...	FS Read [b]
total_fs_write	sum(container_fs_writes_bytes_total{{job="moni...	FS Write [b]

DBMS	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	115989.630678	113481.054475	5563.435273	4517.950883	139965.852845	139650.894833	20039.383417	19866.305729
Q2	13202.835330	13171.131305	423.855536	351.280993	1790.965649	1577.399513	7225.602556	17500.866503
Q3	58803.801925	55998.118738	1121.951630	286.680120	60652.883762	60329.661458	15167.515143	19752.016812
Q4	10906.610491	10700.938849	167.959029	157.038979	10353.898270	10227.802497	63276.056357	3660.092083
Q5	43173.049879	41958.968865	369.634689	383.102751	44849.054096	40186.950882	5783.143584	6885.665454
Q6	19219.394048	19288.650417	1785.649381	129.628404	24070.415718	24026.994607	2917.860596	2886.094683
Q7	35227.129593	33578.747254	2198.463472	3050.601307	27260.769050	24198.993758	11244.580511	13545.375023
Q8	74788.585443	73309.150578	1092.181380	3154.597746	117483.457010	106573.850883	12783.577640	13338.918216
Q9	141097.449787	139187.680818	4918.470218	3963.573644	124488.035032	112492.537863	44068.426727	42587.264411
Q10	141403.858324	142090.442053	908.977431	803.503769	20992.730243	20748.897447	20831.573940	16731.910749
Q11	4148.042627	4293.732452	567.795084	415.515498	5663.032641	5696.939546	3892.319345	2169.532531
Q12	132080.983062	128807.095058	3773.747000	191.421537	34564.011301	34269.547826	15897.897638	7168.397244
Q13	114272.807927	115454.660663	3590.687924	3181.238493	161980.722418	160750.343010	18784.685568	7771.261669
Q14	511364.874354	497102.462734	181.559546	136.743135	29699.836106	29359.513897	8136.222391	3260.906603
Q15	38717.798192	38992.169477	270.773724	282.713636	53015.246627	52652.230879	10828.831254	7963.955605
Q16	4946.922447	4957.202488	1839.745415	1857.860934	5847.584373	5991.898077	7068.259177	6408.576533
Q17	1276.979283	1253.035080	1891.926599	2360.170470	13071.677511	12453.737342	11935.246834	13523.998653
Q18	126016.121606	129203.891398	479.290318	490.452279	30790.192968	30442.444819	33132.461344	29488.654058
Q19	2384.714820	2344.720237	251.887020	248.384294	3420.121102	3349.518520	568.638627	492.559332
Q20	20530.713325	20667.318521	263.041606	233.055218	10182.912607	9965.629668	7626.458291	9463.541198
Q21	246130.899464	245986.749306	71514.882144	70924.262200	81238.430244	78553.948823	6737.272611	6798.701014
Q22	1826.111431	1837.417374	602.300994	969.356511	2372.703644	2338.294653	761.376779	744.457357

DBMS	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	0.008810	0.008846	0.220207	0.231232	0.007174	0.007189	0.050163	0.050691
Q2	0.076588	0.076907	2.789275	3.175859	0.566798	0.639963	0.139435	0.061638
Q3	0.017364	0.018087	3.736227	3.912092	0.016533	0.016633	0.082372	0.052391
Q4	0.092310	0.094059	6.605401	6.746199	0.097566	0.098876	0.407549	0.294164
Q5	0.023296	0.023917	3.608333	3.462481	0.022400	0.024953	0.247183	0.159519
Q6	0.052784	0.052485	7.631666	8.164754	0.041805	0.041873	0.344342	0.349700
Q7	0.028540	0.029938	0.563871	0.485066	0.038508	0.043290	0.099798	0.078069
Q8	0.013432	0.013729	0.959255	0.387856	0.008518	0.009407	0.091922	0.087958
Q9	0.007132	0.007206	0.782999	0.323114	0.008129	0.009099	0.023413	0.023625
Q10	0.007085	0.007049	1.238689	1.262583	0.047705	0.048243	0.050442	0.068517
Q11	0.243337	0.234186	1.938101	2.797194	0.177257	0.177560	0.306996	0.467223
Q12	0.007574	0.007771	4.941322	5.652018	0.029141	0.029371	0.075461	0.145088
Q13	0.008790	0.008695	0.322902	0.343424	0.006207	0.006254	0.054629	0.138944
Q14	0.001958	0.002015	7.144495	7.884286	0.034125	0.034448	0.160541	0.309416
Q15	0.025941	0.025866	3.821016	3.587965	0.019004	0.019073	0.117023	0.126468
Q16	0.205247	0.203905	0.555683	0.565030	0.173094	0.169006	0.147405	0.156668
Q17	0.788392	0.809581	0.695906	0.457047	0.081641	0.084269	0.083890	0.075591
Q18	0.007945	0.007753	2.198075	2.107396	0.032550	0.033012	0.032164	0.036029
Q19	0.429370	0.433899	4.087997	4.146425	0.300284	0.304113	2.180537	2.117862
Q20	0.048879	0.048549	4.019059	4.688444	0.098711	0.100542	0.132065	0.106847
Q21	0.004067	0.004068	0.014502	0.014320	0.012437	0.012804	0.148926	0.149333
Q22	0.550611	0.550844	1.988697	1.171185	0.429374	0.434330	1.372679	1.436696

	0
DBMS
MariaDB-1	1526.198404
MariaDB-2	1526.198404
MonetDB-1	1931.119997
MonetDB-2	1931.119997
MySQL-1	1963.867765
MySQL-2	1963.867765
PostgreSQL-1	472.864693
PostgreSQL-2	472.864693

	0
DBMS
MariaDB-1	1526.761004
MariaDB-2	1526.761004
MonetDB-1	1887.024304
MonetDB-2	1887.024304
MySQL-1	1962.119638
MySQL-2	1962.119638
PostgreSQL-1	464.746812
PostgreSQL-2	464.746812

	0
DBMS
MariaDB-1-1	5564.712371
MariaDB-2-1	5497.702421
MonetDB-1-1	1356.184954
MonetDB-2-1	1218.550218
MySQL-1-1	3011.705555
MySQL-2-1	2896.443679
PostgreSQL-1-1	1436.189579
PostgreSQL-2-1	1278.603392

	0
DBMS
MariaDB-1-1	5580.313389
MariaDB-2-1	5519.121645
MonetDB-1-1	1508.432769
MonetDB-2-1	1368.634461
MySQL-1-1	3029.972219
MySQL-2-1	2915.922365
PostgreSQL-1-1	1492.224116
PostgreSQL-2-1	1331.659788

	total_timer_run
DBMS
MariaDB-1-1	84.432242
MariaDB-2-1	83.348424
MonetDB-1-1	4.717192
MonetDB-2-1	4.458597
MySQL-1-1	45.625206
MySQL-2-1	43.901729
PostgreSQL-1-1	14.941245
PostgreSQL-2-1	11.454957

	total_timer_run
DBMS
MariaDB-1-1	31.517934
MariaDB-2-1	31.257281
MonetDB-1-1	0.752332
MonetDB-2-1	0.792416
MySQL-1-1	23.157694
MySQL-2-1	22.424075
PostgreSQL-1-1	7.769965
PostgreSQL-2-1	7.347514

DBMS	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	115984.029097	113477.396985	5559.138363	4515.038655	139963.454816	139644.360665	20034.833936	19861.297945
Q2	13189.844139	13157.687652	413.282013	340.652305	1782.370459	1560.817159	7211.191122	17486.172169
Q3	58800.386259	55994.774677	1118.389753	283.285230	60650.473856	60324.399922	15162.580179	19747.151356
Q4	10906.366797	10700.690025	167.731983	156.817029	10353.680159	10227.460235	63275.764168	3659.762128
Q5	43172.769596	41958.683499	369.404377	382.882849	44848.791658	40186.573100	5782.834073	6885.320396
Q6	19219.304236	19288.558926	1785.574434	129.571968	24070.314904	24026.849064	2917.765952	2885.997845
Q7	35226.790069	33578.401355	2198.102969	3050.271067	27260.520071	24198.575423	11244.196273	13544.882668
Q8	74788.269761	73308.950132	1091.897862	3154.334292	117483.286765	106573.598974	12783.235100	13338.417255
Q9	141087.924653	139178.495053	4908.692492	3954.063788	124482.944844	112482.017125	44059.743012	42577.751900
Q10	141401.555155	142088.174021	906.461463	801.192185	20991.290802	20745.993865	20828.577831	16729.723187
Q11	3943.081469	4038.932427	260.222192	114.384230	5474.876384	5460.062068	3688.202220	1934.406897
Q12	132080.785154	128806.873278	3773.600698	191.276890	34563.874573	34269.322594	15897.675939	7168.201096
Q13	114271.550681	115453.225490	3589.015730	3179.584549	161979.926707	160748.794775	18783.155397	7769.695470
Q14	511364.784783	497102.376689	181.431396	136.607647	29699.769745	29359.402171	8135.799106	3260.492730
Q15	38717.798192	38992.169477	270.773724	282.713636	53015.246627	52652.230879	10828.831254	7963.955605
Q16	4027.831425	4120.540270	686.746840	691.220624	5078.042286	5088.938285	6058.316758	5587.771454
Q17	1276.899524	1252.970511	1891.844391	2360.095346	13071.609235	12453.614161	11934.943291	13523.710171
Q18	126002.332541	129191.025399	465.953219	477.032062	30781.519091	30425.720738	33115.222733	29473.692810
Q19	2384.642124	2344.654581	251.829252	248.328930	3420.048188	3349.401808	568.557070	492.506636
Q20	20484.981706	20624.654693	230.338829	208.398596	10160.403924	9924.714682	7587.395355	9418.309655
Q21	246127.850226	245983.909848	71511.727077	70921.302693	81236.928218	78550.780588	6734.338372	6795.589572
Q22	1825.728476	1837.013443	600.653826	967.767178	2372.459627	2337.835912	761.021456	744.030803

DBMS	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	25.6731	25.1178	1.2314	1.0000	30.9799	30.9102	4.4355	4.3972
Q2	37.5848	37.4946	1.2066	1.0000	5.0984	4.4904	20.5693	49.8201
Q3	205.1199	195.3331	3.9136	1.0000	211.5699	210.4424	52.9075	68.8992
Q4	69.4516	68.1419	1.0695	1.0000	65.9320	65.1291	402.9322	23.3069
Q5	116.7992	113.5147	1.0000	1.0364	121.3335	108.7207	15.6456	18.6283
Q6	148.2653	148.7996	13.7751	1.0000	185.6878	185.3529	22.5094	22.2644
Q7	16.0235	15.2737	1.0000	1.3876	12.3999	11.0072	5.1147	6.1613
Q8	68.4763	67.1218	1.0000	2.8883	107.5677	97.5789	11.7046	12.2131
Q9	35.5985	35.1167	1.2409	1.0000	31.4080	28.3816	11.1184	10.7447
Q10	175.9841	176.8386	1.1313	1.0000	26.1265	25.8230	25.9259	20.8237
Q11	9.9829	10.3335	1.3665	1.0000	13.6289	13.7105	9.3674	5.2213
Q12	690.0006	672.8976	19.7143	1.0000	180.5649	179.0266	83.0518	37.4482
Q13	35.9209	36.2924	1.1287	1.0000	50.9175	50.5307	5.9048	2.4428
Q14	3739.6018	3635.3011	1.3277	1.0000	217.1943	214.7056	59.5000	23.8469
Q15	142.9895	144.0028	1.0000	1.0441	195.7917	194.4510	39.9922	29.4118
Q16	2.6889	2.6945	1.0000	1.0098	3.1785	3.2569	3.8420	3.4834
Q17	1.0191	1.0000	1.5099	1.8836	10.4320	9.9389	9.5251	10.7930
Q18	262.9223	269.5733	1.0000	1.0233	64.2412	63.5157	69.1282	61.5257
Q19	9.6009	9.4399	1.0141	1.0000	13.7695	13.4852	2.2894	1.9831
Q20	88.0938	88.6799	1.1287	1.0000	43.6931	42.7608	32.7238	40.6064
Q21	36.5327	36.5113	10.6148	10.5271	12.0581	11.6596	1.0000	1.0091
Q22	3.0319	3.0507	1.0000	1.6094	3.9394	3.8823	1.2641	1.2360

	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	1344	1344	1344	1344	1344	1344	1344	1344
Q2	19584	19584	19584	19584	19584	19584	19584	19584
Q3	1344	1344	1344	1344	1344	1344	1344	1344
Q4	624	624	624	624	624	624	624	624
Q5	624	624	624	624	624	624	624	624
Q6	408	408	408	408	408	408	408	408
Q7	768	768	768	768	768	768	768	768
Q8	480	480	480	480	480	480	480	480
Q9	12984	12984	12984	12984	12984	12984	12984	12984
Q10	4224	4224	4224	4224	4224	4224	4224	4224
Q11	455728	455728	455728	455728	455728	455728	455728	455728
Q12	528	528	528	528	528	528	528	528
Q13	2544	2544	2544	2544	2544	2544	2544	2544
Q14	408	408	408	408	408	408	408	408
Q15	0	0	0	0	0	0	0	0
Q16	2746656	2746656	2746656	2746656	2746656	2746656	2746656	2746656
Q17	408	408	408	408	408	408	408	408
Q18	13488	13488	13488	13488	13488	13488	13488	13488
Q19	408	408	408	408	408	408	408	408
Q20	85568	85568	85568	85568	85568	85568	85568	85568
Q21	5184	5184	5184	5184	5184	5184	5184	5184
Q22	888	888	888	888	888	888	888	888

	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	False	False	False	False	False	False	False	False
Q2	False	False	False	False	False	False	False	False
Q3	False	False	False	False	False	False	False	False
Q4	False	False	False	False	False	False	False	False
Q5	False	False	False	False	False	False	False	False
Q6	False	False	False	False	False	False	False	False
Q7	False	False	False	False	False	False	False	False
Q8	False	False	False	False	False	False	False	False
Q9	False	False	False	False	False	False	False	False
Q10	False	False	False	False	False	False	False	False
Q11	False	False	False	False	False	False	False	False
Q12	False	False	False	False	False	False	False	False
Q13	False	False	False	False	False	False	False	False
Q14	False	False	False	False	False	False	False	False
Q15	False	False	False	False	False	False	False	False
Q16	False	False	False	False	False	False	False	False
Q17	False	False	False	False	False	False	False	False
Q18	False	False	False	False	False	False	False	False
Q19	False	False	False	False	False	False	False	False
Q20	False	False	False	False	False	False	False	False
Q21	False	False	False	False	False	False	False	False
Q22	False	False	False	False	False	False	False	False

	DELTA	numRun
1	71	0
2	97	1
3	73	2

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	...	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20	Q21	Q22
MariaDB-1-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
MariaDB-2-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
MonetDB-1-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
MonetDB-2-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
MySQL-1-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
MySQL-2-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
PostgreSQL-1-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0
PostgreSQL-2-1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	NaN	1.0	1.0	1.0	1.0	1.0	1.0	1.0

	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	347.973955	340.447334	16.845194	13.651159	419.900614	418.959277	60.128597	59.610011
Q2	39.615576	39.520509	1.823718	1.384478	5.376375	4.740327	21.686897	52.512813
Q3	176.414484	167.996884	3.498522	0.976042	181.960522	180.991944	45.508243	59.262894
Q4	32.722405	32.105238	0.549320	0.513998	31.063408	30.685955	189.832833	10.984629
Q5	129.521532	125.879449	1.155652	1.228914	134.548819	120.563560	17.351747	20.659699
Q6	57.660592	57.868211	5.364403	0.395960	72.212753	72.083435	8.756129	8.660637
Q7	105.684266	100.738883	6.706806	12.678075	81.784196	72.600099	33.736559	40.639116
Q8	224.372421	219.930033	3.325128	9.827672	352.452032	319.723975	38.353090	40.019183
Q9	423.299235	417.569854	15.697046	15.665570	373.467877	337.484744	132.212075	127.769052
Q10	424.215546	426.275221	3.142779	3.154876	62.980595	62.250786	62.501992	50.201007
Q11	12.636782	13.036927	1.998065	1.483094	17.120079	17.255366	11.835612	6.707820
Q12	396.245450	386.423815	11.375542	0.624792	103.693792	102.811258	47.698360	21.510053
Q13	342.821134	346.367399	10.778061	9.548804	485.944128	482.254182	56.357074	23.317058
Q14	1534.097118	1491.309947	0.554613	0.420054	89.101176	88.081257	24.410784	9.784663
Q15	116.154487	116.977704	0.821195	0.856493	159.046659	157.957757	32.487656	23.893080
Q16	15.720677	15.581426	6.722981	6.640638	18.174277	18.795835	22.066576	19.974487
Q17	3.832843	3.760958	5.683618	7.087494	39.216798	37.363785	35.808117	40.574290
Q18	378.056357	387.620047	1.627102	3.110095	92.376161	91.482406	99.406812	88.473750
Q19	7.156457	7.036084	0.770288	0.758979	10.261899	10.050805	1.708093	1.479426
Q20	61.613432	62.027928	0.891396	0.792113	30.721453	30.143051	23.013910	28.578599
Q21	738.396596	737.964908	214.951881	214.146436	243.717694	235.666268	20.215939	20.400054
Q22	5.480514	5.514622	1.872112	3.753415	7.119875	7.017448	2.286109	2.236404

	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	20.743404	20.294728	1.004175	0.813772	25.031092	24.974977	3.584383	3.553469
Q2	23.770197	23.713155	1.094270	0.830717	3.225941	2.844298	13.012605	31.508817
Q3	21.603285	20.572486	0.428420	0.119524	22.282439	22.163829	5.572828	7.257189
Q4	9.962439	9.774540	0.167242	0.156488	9.457352	9.342435	57.795200	3.344305
Q5	23.510497	22.849393	0.209772	0.223070	24.423041	21.884463	3.149655	3.750109
Q6	20.374615	20.447978	1.895534	0.139914	25.516683	25.470988	3.094016	3.060273
Q7	23.249385	22.161455	1.475424	2.789038	17.991631	15.971230	7.421675	8.940162
Q8	18.573822	18.206075	0.275258	0.813547	29.176407	26.467139	3.174915	3.312837
Q9	22.965884	22.655039	0.851635	0.849927	20.262309	18.310062	7.173099	6.932045
Q10	38.750956	38.939101	0.287084	0.288190	5.753109	5.686443	5.709390	4.585728
Q11	15.396863	15.884406	2.434475	1.807026	20.859386	21.024221	14.420704	8.172919
Q12	37.019032	36.101451	1.062754	0.058371	9.687540	9.605090	4.456195	2.009566
Q13	19.507426	19.709218	0.613300	0.543352	27.651502	27.441534	3.206866	1.326802
Q14	47.381440	46.059934	0.017130	0.012974	2.751939	2.720438	0.753941	0.302205
Q15	19.098230	19.233584	0.135022	0.140825	26.150602	25.971563	5.341651	3.928523
Q16	12.711086	12.598494	5.435923	5.369344	14.694965	15.197531	17.842116	16.150540
Q17	2.211325	2.169851	3.279113	4.089067	22.625785	21.556705	20.659177	23.408978
Q18	33.100333	33.937672	0.142459	0.272301	8.087899	8.009647	8.703460	7.746228
Q19	18.246015	17.939112	1.963916	1.935083	26.163610	25.625407	4.354933	3.771925
Q20	25.911744	26.086061	0.374880	0.333126	12.920014	12.676765	9.678580	12.018829
Q21	30.443572	30.425774	8.862315	8.829107	10.048309	9.716354	0.833489	0.841080
Q22	15.534117	15.630793	5.306365	10.638782	20.180767	19.890444	6.479809	6.338923

	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	False	False	True	True	False	False	False	False
Q2	False	False	False	False	False	False	False	False
Q3	False	False	False	False	False	False	False	False
Q4	False	False	False	False	False	False	False	False
Q5	False	False	False	False	False	False	False	False
Q6	False	False	False	False	False	False	False	False
Q7	False	False	False	False	False	False	False	False
Q8	False	False	False	False	False	False	False	False
Q9	False	False	False	False	False	False	False	False
Q10	False	False	False	False	False	False	False	False
Q11	False	False	False	False	False	False	False	False
Q12	False	False	False	False	False	False	False	False
Q13	False	False	False	False	False	False	False	False
Q14	False	False	False	False	False	False	False	False
Q15	True	False	True	True	True	True	True	True
Q16	False	False	False	False	False	False	False	False
Q17	False	False	False	False	False	False	False	False
Q18	False	False	False	False	False	False	False	False
Q19	False	False	False	False	False	False	False	False
Q20	False	False	False	False	False	False	False	False
Q21	False	False	False	False	False	False	False	False
Q22	False	False	False	False	False	False	False	False

	MariaDB-1-1	MariaDB-2-1	MonetDB-1-1	MonetDB-2-1	MySQL-1-1	MySQL-2-1	PostgreSQL-1-1	PostgreSQL-2-1
Q1	165.947234	162.357822	8.033398	6.510177	200.248739	199.799819	28.675061	28.427749
Q2	190.161578	189.705241	8.754162	6.645734	25.807525	22.754386	104.100838	252.070536
Q3	172.826279	164.579890	3.427363	0.956189	178.259513	177.310635	44.582622	58.057509
Q4	79.699508	78.196322	1.337938	1.251906	75.658813	74.739478	462.361598	26.754437
Q5	188.083977	182.795146	1.678173	1.784561	195.384324	175.075706	25.197243	30.000868
Q6	162.996918	163.583822	15.164276	1.119312	204.133462	203.767902	24.752124	24.482184
Q7	185.995083	177.291640	11.803393	22.312305	143.933047	127.769837	59.373398	71.521296
Q8	148.590573	145.648602	2.202065	6.508373	233.411259	211.737113	25.399323	26.502692
Q9	183.727070	181.240313	6.813082	6.799420	162.098471	146.480499	57.384788	55.456357
Q10	310.007644	311.512811	2.296676	2.305516	46.024871	45.491542	45.675118	36.685822
Q11	123.174905	127.075251	19.475797	14.456209	166.875087	168.193769	115.365632	65.383349
Q12	296.152257	288.811606	8.502034	0.466967	77.500324	76.840721	35.649562	16.076527
Q13	156.059409	157.673743	4.906400	4.346817	221.212014	219.532272	25.654928	10.614417
Q14	379.051518	368.479474	0.137036	0.103789	22.015514	21.763508	6.031525	2.417638
Q15	152.785842	153.868675	1.080173	1.126603	209.204813	207.772505	42.733208	31.428182
Q16	101.688692	100.787949	43.487385	42.954751	117.559722	121.580250	142.736932	129.204320
Q17	17.690600	17.358810	26.232903	32.712534	181.006278	172.453643	165.273412	187.271821
Q18	264.802664	271.501376	1.139674	2.178409	64.703193	64.077179	69.627684	61.969821
Q19	145.968117	143.512895	15.711325	15.480666	209.308878	205.003257	34.839461	30.175402
Q20	207.293950	208.688491	2.999039	2.665008	103.360114	101.414122	77.428642	96.150635
Q21	243.548577	243.406191	70.898518	70.632855	80.386472	77.730835	6.667912	6.728639
Q22	124.272938	125.046343	42.450923	85.110253	161.446139	159.123549	51.838470	50.711385

	0	1	2
DBMS
MariaDB-1-1	10832.749509	10974.072207	10912.278674
MariaDB-2-1	10631.296530	10786.618634	10684.154912
MonetDB-1-1	188.181439	151.178077	163.836433
MonetDB-2-1	167.151919	148.022101	155.277067
MySQL-1-1	10249.201583	10490.168055	10321.670839
MySQL-2-1	10113.206561	10378.973982	10190.200162
PostgreSQL-1-1	184856.501106	2517.352709	2453.438690
PostgreSQL-2-1	3520.991707	3399.093443	4059.201233

	factor	n	Mean	Std Dev	cv [%]	Median	iqr	qcod [%]	Min	Max	Range	Geo	1st	Last	Sum	P25	P75	P90	P95
DBMS
MariaDB-1-1	69.5484	3.0	10906.366797	57.845996	0.530387	10912.278674	70.661349	0.323902	10832.749509	10974.072207	141.322698	10906.213308	10832.749509	10912.278674	32719.100390	10872.514091	10943.175440	10961.713500	10967.892854
MariaDB-2-1	68.2368	3.0	10700.690025	64.478919	0.602568	10684.154912	77.661052	0.363019	10631.296530	10786.618634	155.322104	10700.496044	10631.296530	10684.154912	32102.070076	10657.725721	10735.386773	10766.125890	10776.372262
MonetDB-1-1	1.0696	3.0	167.731983	15.355643	9.154869	163.836433	18.501681	5.547461	151.178077	188.181439	37.003362	167.041877	188.181439	163.836433	503.195949	157.507255	176.008936	183.312438	185.746938
MonetDB-2-1	1.0000	3.0	156.817029	7.885265	5.028322	155.277067	9.564909	3.057209	148.022101	167.151919	19.129818	156.620432	167.151919	155.277067	470.451087	151.649584	161.214493	164.776949	165.964434
MySQL-1-1	66.0240	3.0	10353.680159	100.944399	0.974962	10321.670839	120.483236	0.582288	10249.201583	10490.168055	240.966472	10353.189470	10249.201583	10321.670839	31061.040476	10285.436211	10405.919447	10456.468611	10473.318333
MySQL-2-1	65.2191	3.0	10227.460235	111.652184	1.091690	10190.200162	132.883710	0.650234	10113.206561	10378.973982	265.767421	10226.852806	10113.206561	10190.200162	30682.380705	10151.703362	10284.587072	10341.219218	10360.096600
PostgreSQL-1-1	403.5006	3.0	63275.764168	85970.567510	135.866502	2517.352709	91201.531208	94.831370	2453.438690	184856.501106	182403.062416	10451.645460	184856.501106	2453.438690	189827.292505	2485.395700	93686.926907	148388.671427	166622.586266
PostgreSQL-2-1	23.3378	3.0	3659.762128	286.796671	7.836484	3520.991707	330.053895	4.552380	3399.093443	4059.201233	660.107790	3648.851839	3520.991707	4059.201233	10979.286383	3460.042575	3790.096470	3951.559328	4005.380280

Table of Contents

Load Evaluation of Benchmarks¶

Inspect Result Folder¶

Pick an Experiment and load it¶

Load general Properties into Variables¶

Show Properties of the Workload¶

Show Properties of a DBMS¶

Properties of MonetDB-1-1¶

Show Properties of a Query¶

Errors of Query 1¶

Warnings of Query 1¶

Show Query Template 1 - Pricing Summary Report (TPC-H Q1)¶

Show Parameters of Query 1 - Pricing Summary Report (TPC-H Q1)¶

Show Query 1 as run by MariaDB-1-1 - Run number 0¶

Show Query 1 as run by MonetDB-1-1 - Run number 0¶

Show Result Set of Query 1 as run by MariaDB-1-1 - Run number 0¶

Show Result Set of Query 1 as run by MonetDB-1-1 - Run number 0¶

Some Measures of the Workload¶

Hardware Metrics¶

Hardware Metrics¶

CPU of Ingestion (via counter)¶

CPU of Ingestion (via rate)¶

CPU of Stream (via counter)¶

CPU of Stream (via rate)¶

Timing Measures¶

Mean of Means of Timer Run [s]¶

Geometric Mean of Medians of Timer Run [s]¶

Plots¶

Some Measures per Query¶

Timing Measures¶

Means of Timer Runs [ms]¶

Maximum of Run Throughput [1/s]¶

Latency of Timer Execution [ms]¶

Latency of Timer Connection [ms]¶

Latency of Timer Data Transfer [ms]¶

Latency of Timer Run - normalized to 1 per Query¶

Size of Result Sets per Query¶

Size of Result Sets per Query - normalized to 1¶

Table of Errors¶

Table of Warnings¶

Total Time [s] per Query¶

Total Time per Query - normalized to 100%¶

Total Time per Query - normalized to 100%¶

Plots¶

Inspect Single Queries¶

Measures¶

Measures of Execution Times - 3 Runs of Query 4¶

Statistics¶

Statistics of Execution Times - 3 Runs of Query 4¶

Plots¶